N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks
Loading...
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies.We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30??45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physicallybased cloth simulators.
Description
CCS Concepts: Computing methodologies --> Machine learning; Physical simulation
@article{10.1111:cgf.14493,
journal = {Computer Graphics Forum},
title = {{N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks}},
author = {Li, Yu Di and Tang, Min and Yang, Yun and Huang, Zi and Tong, Ruo Feng and Yang, Shuang Cai and Li, Yao and Manocha, Dinesh},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14493}
}